Designing inference for scale
Your prototype ran smoothly. Now usage is growing, context windows are expanding, and agentic workflows are stacking inference calls on top of each other. At scale, memory coordination becomes the bottleneck compute can't fix.
This guide explains why modern AI workloads place structural pressure on inference, where conventional serving architectures hit a wall, and what coordination-first infrastructure looks like in production.
Inside, you’ll find:
This guide explains why modern AI workloads place structural pressure on inference, where conventional serving architectures hit a wall, and what coordination-first infrastructure looks like in production.
Inside, you’ll find:
- The three metrics that define production inference performance
- Where paged memory, session affinity, and prefill/decode disaggregation each hit their ceiling
- How cluster-level memory coordination can drastically speed up time-to-first-token (TTFT), reduce latency, and increase throughput